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Pave Bank is a digital bank serving globally-operating businesses, particularly in digital asset trading, payments, stablecoins, and fintech. The Commercial team owns client relationships from acquisition through revenue growth.
You will be a Data Analyst embedded within the Commercial team, responsible for transforming client, transaction, and pipeline data into actionable decisions. This is not a passive reporting role—you will build dashboards, models, and internal tools that the Commercial team uses daily, and you will deploy them yourself.
Key responsibilities include:
COMMERCIAL ANALYTICS: Analyze client revenue, transaction volumes, payment corridors, and product usage to identify growth drivers. Build and maintain client profitability and unit economics models covering fee income, correspondent costs, and FX spreads. Track pipeline health metrics (conversion rates, onboarding time, time to first transaction) and explain underlying movements. Identify at-risk clients through activity pattern changes and spot cross-sell/upsell opportunities. Support pricing decisions with scenario modeling for fee changes and volume tiers. Prepare defensible data for board, investor, and management reporting.
DASHBOARDS AND REPORTING: Design and own the Commercial team's dashboard layer covering revenue, pipeline, client activity, and product adoption. Migrate the team from manual spreadsheets to live, self-serve reporting. Define metrics clearly and consistently with documentation to prevent drift. Collaborate with Operations, Finance, Compliance, and Product to ensure data trustworthiness.
TOOLING AND AUTOMATION: Build internal tools solving real Commercial problems (client scoring, pricing calculators, lead enrichment, reporting automation, data quality checks). Deploy and host tools on Google Cloud. Automate recurring manual Commercial workflows. Write clean, documented, maintainable code.
AI ADOPTION: Use Claude Code and agentic tools as your primary build environment. Build AI-assisted workflows into Commercial processes (summarizing client interactions, drafting documents, structuring unstructured data). Set the standard for AI usage within the team. Apply judgment on where AI output requires human verification.
Required: 3+ years in analytical roles (fintech, payments, banking, or data-heavy commercial environments preferred). Strong SQL for complex queries against large transaction datasets. Python for analysis and building applications. Hands-on Google Cloud experience (Cloud Run, Cloud Functions, App Engine, BigQuery, Cloud Scheduler). Modern BI tool experience (Looker Studio, Metabase, Tableau, Power BI). Daily practical use of AI coding tools with demonstrated examples. Advanced spreadsheet skills including modeling and Google Sheets automation. Strong commercial instinct. Fluent professional English (mandatory); Georgian is an advantage.
Nice to have: Payments mechanics knowledge (SWIFT, SEPA, correspondent banking, card settlement, stablecoin flows). Experience with dbt, Airflow, or similar transformation/orchestration tools. CRM data models and pipeline analytics familiarity. Front-end skills (React, Streamlit). Digital assets or regulated banking environment exposure.
The team is small with wide scope—you own work end-to-end. AI tooling is default for achieving meaningful output. You have direct access to decision makers, and good analysis drives quick decisions. The culture favors shipping working tools over perfect designs.
